Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community

Abstract

With the fast development of microgrids (MGs), the microgrid community (MGC) integrating adjacent MGs has raised more and more attentions. In an MGC, each MG shares available power reciprocally to minimize the operation costs and maintain power balance. As the amount of MGs grows, the MGC becomes complicated and difficult to control. Different from centralized strategy, this paper proposes a cooperative energy management scheme based on distributed model predictive control (DMPC) for grid-connected MGC. The normal MGC is virtualized as two-level structure to simplify the internal interactions among subsystems. All the subsystems iterate in parallel adopting the proposed cooperative logarithmic-barrier method, which follows a communication protocol to derive control signals. The real-time hardware-in-the-loop (HIL) study verifies that, the optimization results of proposed DMPC approximate to Pareto solutions of centralized MPC (CMPC), whereas the computing speed is much faster than CMPC.